Simple estimation of semiparametric models with measurement errors - Kirill S. Evdokimov; Andrei Zeleneev (Centre for Microdata Methods and Practice (cemmap), 2022)
We develop a practical way of addressing the Errors-In-Variables (EIV) problem in the Generalized Method of Moments (GMM) framework. We focus on the settings in which the variance of the measurement errors is a fraction of that of the mismeasured variables, which is typical for empirical applications. For any initial set of moment conditions our approach provides a 'corrected' set of moment conditions that do not suffer from the EIV bias. The EIV-robust estimator is then computed as a standard GMM estimator with these corrected moment conditions. We show that our estimator is Ín-consistent, and that the standard tests and confidence intervals provide valid inference. This is true even when the EIV are so large that the naive estimator (that ignores the EIV problem) may have a large bias with confidence intervals having 0% coverage. Our approach requires no nonparametric estimation, which can be particularly useful when the measurement errors are multivariate, serially correlated, or non-classical.
Keywords: 330; Errors-In-Variables; measurement error robust GMM; moment conditions; nonstandard asymptotic approximation